Importance of Spatial Population Characteristics on the Fertilization Rates of Sea Urchins
Bibliographic record
Abstract
We show that inclusion of population characteristics in coupled advection-diffusion and fertilization-kinetics models results in higher fertilization rates than those previously reported in theoretical studies. We incorporate parameters related to both individuals and populations by running simulations over a large spatial scale and incorporating sperm contribution from multiple males. We compare predictions for three subpopulations of the sea urchin Strongylocentrotus droebachiensis (those occupying kelp beds, barrens, and grazing fronts) to observations from small-scale experiments, and estimate effects of population size and current velocity in each subpopulation. Model outputs suggest that fertilization rates are low in kelp beds, intermediate in barrens, and high in grazing fronts. In all populations, increasing current velocity has a negative effect on the relationship between fertilization rate and downstream distance of gametes after release, but no effect on the relationship between fertilization rate and elapsed time since gamete release. Our model output was most sensitive to changes in the number of spawning males and the sperm release rate, suggesting that spawning synchrony and high gonadic index could greatly increase the fertilization success in sea urchins.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".